Overview

Dataset statistics

Number of variables13
Number of observations1601
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory162.7 KiB
Average record size in memory104.1 B

Variable types

Numeric13

Alerts

fixed_acidity is highly overall correlated with citric_acid and 2 other fieldsHigh correlation
volatile_acidity is highly overall correlated with citric_acidHigh correlation
citric_acid is highly overall correlated with fixed_acidity and 2 other fieldsHigh correlation
free_sulfur_dioxide is highly overall correlated with total_sulfur_dioxideHigh correlation
total_sulfur_dioxide is highly overall correlated with free_sulfur_dioxideHigh correlation
density is highly overall correlated with fixed_acidityHigh correlation
pH is highly overall correlated with fixed_acidity and 1 other fieldsHigh correlation
ID is uniformly distributedUniform
ID has unique valuesUnique
citric_acid has 132 (8.2%) zerosZeros

Reproduction

Analysis started2023-03-24 17:37:07.324999
Analysis finished2023-03-24 17:38:08.042523
Duration1 minute and 0.72 seconds
Software versionydata-profiling vv4.1.1
Download configurationconfig.json

Variables

ID
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct1601
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean801
Minimum1
Maximum1601
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.6 KiB
2023-03-24T17:38:08.322112image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile81
Q1401
median801
Q31201
95-th percentile1521
Maximum1601
Range1600
Interquartile range (IQR)800

Descriptive statistics

Standard deviation462.31321
Coefficient of variation (CV)0.57717004
Kurtosis-1.2
Mean801
Median Absolute Deviation (MAD)400
Skewness0
Sum1282401
Variance213733.5
MonotonicityStrictly increasing
2023-03-24T17:38:08.921659image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
1065 1
 
0.1%
1075 1
 
0.1%
1074 1
 
0.1%
1073 1
 
0.1%
1072 1
 
0.1%
1071 1
 
0.1%
1070 1
 
0.1%
1069 1
 
0.1%
1068 1
 
0.1%
Other values (1591) 1591
99.4%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1601 1
0.1%
1600 1
0.1%
1599 1
0.1%
1598 1
0.1%
1597 1
0.1%
1596 1
0.1%
1595 1
0.1%
1594 1
0.1%
1593 1
0.1%
1592 1
0.1%

fixed_acidity
Real number (ℝ)

Distinct97
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.319875
Minimum4.6
Maximum15.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.6 KiB
2023-03-24T17:38:09.350536image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum4.6
5-th percentile6.1
Q17.1
median7.9
Q39.2
95-th percentile11.8
Maximum15.9
Range11.3
Interquartile range (IQR)2.1

Descriptive statistics

Standard deviation1.7394949
Coefficient of variation (CV)0.20907705
Kurtosis1.140393
Mean8.319875
Median Absolute Deviation (MAD)1
Skewness0.98476138
Sum13320.12
Variance3.0258425
MonotonicityNot monotonic
2023-03-24T17:38:09.739654image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.2 68
 
4.2%
7.1 57
 
3.6%
7.8 53
 
3.3%
7.5 52
 
3.2%
7 50
 
3.1%
7.7 50
 
3.1%
6.8 46
 
2.9%
7.6 46
 
2.9%
8.2 45
 
2.8%
7.4 44
 
2.7%
Other values (87) 1090
68.1%
ValueCountFrequency (%)
4.6 1
 
0.1%
4.7 1
 
0.1%
4.9 1
 
0.1%
5 6
0.4%
5.1 4
 
0.2%
5.2 6
0.4%
5.3 4
 
0.2%
5.4 5
 
0.3%
5.5 1
 
0.1%
5.6 14
0.9%
ValueCountFrequency (%)
15.9 1
0.1%
15.6 2
0.1%
15.5 2
0.1%
15 2
0.1%
14.3 1
0.1%
14 1
0.1%
13.8 1
0.1%
13.7 2
0.1%
13.5 1
0.1%
13.4 1
0.1%

volatile_acidity
Real number (ℝ)

Distinct143
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52774204
Minimum0.12
Maximum1.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.6 KiB
2023-03-24T17:38:10.139720image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.12
5-th percentile0.27
Q10.39
median0.52
Q30.64
95-th percentile0.84
Maximum1.58
Range1.46
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.17898117
Coefficient of variation (CV)0.33914518
Kurtosis1.2290649
Mean0.52774204
Median Absolute Deviation (MAD)0.12
Skewness0.67266692
Sum844.915
Variance0.032034258
MonotonicityNot monotonic
2023-03-24T17:38:10.518191image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6 47
 
2.9%
0.5 46
 
2.9%
0.43 43
 
2.7%
0.59 39
 
2.4%
0.58 38
 
2.4%
0.36 38
 
2.4%
0.4 37
 
2.3%
0.39 36
 
2.2%
0.38 35
 
2.2%
0.49 35
 
2.2%
Other values (133) 1207
75.4%
ValueCountFrequency (%)
0.12 3
 
0.2%
0.16 2
 
0.1%
0.18 10
0.6%
0.19 2
 
0.1%
0.2 3
 
0.2%
0.21 6
0.4%
0.22 6
0.4%
0.23 5
 
0.3%
0.24 13
0.8%
0.25 7
0.4%
ValueCountFrequency (%)
1.58 1
 
0.1%
1.33 2
0.1%
1.24 1
 
0.1%
1.185 1
 
0.1%
1.18 1
 
0.1%
1.13 1
 
0.1%
1.115 1
 
0.1%
1.09 1
 
0.1%
1.07 1
 
0.1%
1.04 3
0.2%

citric_acid
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct81
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.271175
Minimum0
Maximum1
Zeros132
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size12.6 KiB
2023-03-24T17:38:10.949170image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.09
median0.26
Q30.42
95-th percentile0.6
Maximum1
Range1
Interquartile range (IQR)0.33

Descriptive statistics

Standard deviation0.1946837
Coefficient of variation (CV)0.71792644
Kurtosis-0.78766691
Mean0.271175
Median Absolute Deviation (MAD)0.17
Skewness0.31620138
Sum434.15117
Variance0.037901744
MonotonicityNot monotonic
2023-03-24T17:38:11.349173image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 132
 
8.2%
0.49 68
 
4.2%
0.24 51
 
3.2%
0.02 50
 
3.1%
0.26 39
 
2.4%
0.1 35
 
2.2%
0.21 33
 
2.1%
0.01 33
 
2.1%
0.08 33
 
2.1%
0.32 32
 
2.0%
Other values (71) 1095
68.4%
ValueCountFrequency (%)
0 132
8.2%
0.01 33
 
2.1%
0.02 50
 
3.1%
0.03 30
 
1.9%
0.04 29
 
1.8%
0.05 20
 
1.2%
0.06 24
 
1.5%
0.07 22
 
1.4%
0.08 33
 
2.1%
0.09 30
 
1.9%
ValueCountFrequency (%)
1 1
 
0.1%
0.79 1
 
0.1%
0.78 1
 
0.1%
0.76 3
0.2%
0.75 1
 
0.1%
0.74 4
0.2%
0.73 3
0.2%
0.72 1
 
0.1%
0.71 1
 
0.1%
0.7 2
0.1%

residual_sugar
Real number (ℝ)

Distinct91
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5384447
Minimum0.9
Maximum15.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.6 KiB
2023-03-24T17:38:11.741067image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile1.6
Q11.9
median2.2
Q32.6
95-th percentile5.1
Maximum15.5
Range14.6
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation1.4091379
Coefficient of variation (CV)0.55511859
Kurtosis28.653519
Mean2.5384447
Median Absolute Deviation (MAD)0.3
Skewness4.5433168
Sum4064.05
Variance1.9856695
MonotonicityNot monotonic
2023-03-24T17:38:12.112223image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 156
 
9.7%
2.2 131
 
8.2%
1.8 129
 
8.1%
2.1 128
 
8.0%
1.9 118
 
7.4%
2.3 109
 
6.8%
2.4 86
 
5.4%
2.5 84
 
5.2%
2.6 80
 
5.0%
1.7 76
 
4.7%
Other values (81) 504
31.5%
ValueCountFrequency (%)
0.9 2
 
0.1%
1.2 8
 
0.5%
1.3 5
 
0.3%
1.4 35
 
2.2%
1.5 30
 
1.9%
1.6 58
3.6%
1.65 2
 
0.1%
1.7 76
4.7%
1.75 2
 
0.1%
1.8 129
8.1%
ValueCountFrequency (%)
15.5 1
0.1%
15.4 2
0.1%
13.9 1
0.1%
13.8 2
0.1%
13.4 1
0.1%
12.9 1
0.1%
11 2
0.1%
10.7 1
0.1%
9 1
0.1%
8.9 1
0.1%

chlorides
Real number (ℝ)

Distinct154
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.087487484
Minimum0.012
Maximum0.611
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.6 KiB
2023-03-24T17:38:12.516362image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.012
5-th percentile0.054
Q10.07
median0.079
Q30.09
95-th percentile0.126
Maximum0.611
Range0.599
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.047031886
Coefficient of variation (CV)0.53758416
Kurtosis41.776562
Mean0.087487484
Median Absolute Deviation (MAD)0.01
Skewness5.6840823
Sum140.06746
Variance0.0022119983
MonotonicityNot monotonic
2023-03-24T17:38:12.900447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.08 66
 
4.1%
0.074 53
 
3.3%
0.076 51
 
3.2%
0.078 51
 
3.2%
0.084 49
 
3.1%
0.077 47
 
2.9%
0.071 47
 
2.9%
0.082 46
 
2.9%
0.075 45
 
2.8%
0.079 43
 
2.7%
Other values (144) 1103
68.9%
ValueCountFrequency (%)
0.012 2
 
0.1%
0.034 1
 
0.1%
0.038 2
 
0.1%
0.039 4
0.2%
0.041 4
0.2%
0.042 3
0.2%
0.043 1
 
0.1%
0.044 5
0.3%
0.045 4
0.2%
0.046 4
0.2%
ValueCountFrequency (%)
0.611 1
 
0.1%
0.61 1
 
0.1%
0.467 1
 
0.1%
0.464 1
 
0.1%
0.422 1
 
0.1%
0.415 3
0.2%
0.414 2
0.1%
0.413 1
 
0.1%
0.403 1
 
0.1%
0.401 1
 
0.1%

free_sulfur_dioxide
Real number (ℝ)

Distinct61
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.886875
Minimum1
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.6 KiB
2023-03-24T17:38:13.306678image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q17
median14
Q321
95-th percentile35
Maximum72
Range71
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.455218
Coefficient of variation (CV)0.65810411
Kurtosis2.0211757
Mean15.886875
Median Absolute Deviation (MAD)7
Skewness1.2477739
Sum25434.887
Variance109.31158
MonotonicityNot monotonic
2023-03-24T17:38:13.706219image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 138
 
8.6%
5 104
 
6.5%
15 78
 
4.9%
10 78
 
4.9%
12 75
 
4.7%
7 71
 
4.4%
9 62
 
3.9%
16 61
 
3.8%
17 60
 
3.7%
11 59
 
3.7%
Other values (51) 815
50.9%
ValueCountFrequency (%)
1 3
 
0.2%
2 1
 
0.1%
3 49
 
3.1%
4 41
 
2.6%
5 104
6.5%
5.5 1
 
0.1%
6 138
8.6%
7 71
4.4%
8 56
3.5%
9 62
3.9%
ValueCountFrequency (%)
72 1
 
0.1%
68 2
0.1%
66 1
 
0.1%
57 1
 
0.1%
55 2
0.1%
54 1
 
0.1%
53 1
 
0.1%
52 3
0.2%
51 4
0.2%
50 2
0.1%

total_sulfur_dioxide
Real number (ℝ)

Distinct145
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.52
Minimum6
Maximum289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.6 KiB
2023-03-24T17:38:14.349184image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11
Q122
median38
Q362
95-th percentile113
Maximum289
Range283
Interquartile range (IQR)40

Descriptive statistics

Standard deviation32.967416
Coefficient of variation (CV)0.70867189
Kurtosis3.7864751
Mean46.52
Median Absolute Deviation (MAD)18
Skewness1.5166037
Sum74478.52
Variance1086.8505
MonotonicityNot monotonic
2023-03-24T17:38:14.712644image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 43
 
2.7%
24 36
 
2.2%
15 35
 
2.2%
18 35
 
2.2%
23 34
 
2.1%
14 33
 
2.1%
20 33
 
2.1%
31 32
 
2.0%
38 31
 
1.9%
27 30
 
1.9%
Other values (135) 1259
78.6%
ValueCountFrequency (%)
6 3
 
0.2%
7 4
 
0.2%
8 14
 
0.9%
9 14
 
0.9%
10 27
1.7%
11 26
1.6%
12 29
1.8%
13 28
1.7%
14 33
2.1%
15 35
2.2%
ValueCountFrequency (%)
289 1
0.1%
278 1
0.1%
165 1
0.1%
160 1
0.1%
155 1
0.1%
153 1
0.1%
152 1
0.1%
151 2
0.1%
149 1
0.1%
148 2
0.1%

density
Real number (ℝ)

Distinct436
Distinct (%)27.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.99674564
Minimum0.99007
Maximum1.00369
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.6 KiB
2023-03-24T17:38:15.115880image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.99007
5-th percentile0.9936
Q10.9956
median0.99675
Q30.99783
95-th percentile1
Maximum1.00369
Range0.01362
Interquartile range (IQR)0.00223

Descriptive statistics

Standard deviation0.0018866715
Coefficient of variation (CV)0.0018928314
Kurtosis0.93535432
Mean0.99674564
Median Absolute Deviation (MAD)0.00113
Skewness0.072381461
Sum1595.7898
Variance3.5595293 × 10-6
MonotonicityNot monotonic
2023-03-24T17:38:15.535534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9972 36
 
2.2%
0.9968 35
 
2.2%
0.9976 35
 
2.2%
0.998 29
 
1.8%
0.9962 28
 
1.7%
0.9978 26
 
1.6%
0.9964 25
 
1.6%
0.997 24
 
1.5%
0.9994 24
 
1.5%
0.9982 23
 
1.4%
Other values (426) 1316
82.2%
ValueCountFrequency (%)
0.99007 2
0.1%
0.9902 1
0.1%
0.99064 2
0.1%
0.9908 1
0.1%
0.99084 1
0.1%
0.9912 1
0.1%
0.9915 1
0.1%
0.99154 1
0.1%
0.99157 1
0.1%
0.9916 2
0.1%
ValueCountFrequency (%)
1.00369 2
0.1%
1.0032 1
 
0.1%
1.00315 3
0.2%
1.00289 1
 
0.1%
1.0026 2
0.1%
1.00242 2
0.1%
1.0022 2
0.1%
1.0021 2
0.1%
1.0018 1
 
0.1%
1.0015 2
0.1%

pH
Real number (ℝ)

Distinct90
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3111062
Minimum2.74
Maximum4.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.6 KiB
2023-03-24T17:38:15.957466image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2.74
5-th percentile3.06
Q13.21
median3.31
Q33.4
95-th percentile3.57
Maximum4.01
Range1.27
Interquartile range (IQR)0.19

Descriptive statistics

Standard deviation0.15429019
Coefficient of variation (CV)0.046597778
Kurtosis0.81171362
Mean3.3111062
Median Absolute Deviation (MAD)0.1
Skewness0.19393841
Sum5301.0811
Variance0.023805464
MonotonicityNot monotonic
2023-03-24T17:38:16.375425image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.3 58
 
3.6%
3.36 56
 
3.5%
3.26 53
 
3.3%
3.38 48
 
3.0%
3.39 48
 
3.0%
3.29 46
 
2.9%
3.32 45
 
2.8%
3.34 43
 
2.7%
3.28 42
 
2.6%
3.35 39
 
2.4%
Other values (80) 1123
70.1%
ValueCountFrequency (%)
2.74 1
 
0.1%
2.86 1
 
0.1%
2.87 1
 
0.1%
2.88 2
0.1%
2.89 4
0.2%
2.9 1
 
0.1%
2.92 4
0.2%
2.93 3
0.2%
2.94 4
0.2%
2.95 1
 
0.1%
ValueCountFrequency (%)
4.01 2
0.1%
3.9 2
0.1%
3.85 1
 
0.1%
3.78 2
0.1%
3.75 1
 
0.1%
3.74 1
 
0.1%
3.72 3
0.2%
3.71 4
0.2%
3.7 1
 
0.1%
3.69 4
0.2%

sulphates
Real number (ℝ)

Distinct96
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.65821986
Minimum0.33
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.6 KiB
2023-03-24T17:38:16.806304image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.33
5-th percentile0.47
Q10.55
median0.62
Q30.73
95-th percentile0.93
Maximum2
Range1.67
Interquartile range (IQR)0.18

Descriptive statistics

Standard deviation0.16947055
Coefficient of variation (CV)0.25746799
Kurtosis11.711201
Mean0.65821986
Median Absolute Deviation (MAD)0.08
Skewness2.4266711
Sum1053.81
Variance0.028720267
MonotonicityNot monotonic
2023-03-24T17:38:17.189973image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6 69
 
4.3%
0.58 68
 
4.2%
0.54 68
 
4.2%
0.62 61
 
3.8%
0.56 60
 
3.7%
0.57 55
 
3.4%
0.59 52
 
3.2%
0.53 51
 
3.2%
0.55 50
 
3.1%
0.63 48
 
3.0%
Other values (86) 1019
63.6%
ValueCountFrequency (%)
0.33 1
 
0.1%
0.37 2
 
0.1%
0.39 6
 
0.4%
0.4 4
 
0.2%
0.42 5
 
0.3%
0.43 8
0.5%
0.44 16
1.0%
0.45 12
0.7%
0.46 18
1.1%
0.47 19
1.2%
ValueCountFrequency (%)
2 1
 
0.1%
1.98 1
 
0.1%
1.95 2
0.1%
1.62 1
 
0.1%
1.61 1
 
0.1%
1.59 1
 
0.1%
1.56 1
 
0.1%
1.36 3
0.2%
1.34 1
 
0.1%
1.33 1
 
0.1%

alcohol
Real number (ℝ)

Distinct65
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.423204
Minimum8.4
Maximum14.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.6 KiB
2023-03-24T17:38:17.594154image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum8.4
5-th percentile9.2
Q19.5
median10.2
Q311.1
95-th percentile12.5
Maximum14.9
Range6.5
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.0654949
Coefficient of variation (CV)0.10222336
Kurtosis0.19816181
Mean10.423204
Median Absolute Deviation (MAD)0.7
Skewness0.85999682
Sum16687.55
Variance1.1352795
MonotonicityNot monotonic
2023-03-24T17:38:17.975743image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.5 139
 
8.7%
9.4 103
 
6.4%
9.8 78
 
4.9%
9.2 72
 
4.5%
10 67
 
4.2%
10.5 67
 
4.2%
9.3 59
 
3.7%
11 59
 
3.7%
9.6 59
 
3.7%
9.7 55
 
3.4%
Other values (55) 843
52.7%
ValueCountFrequency (%)
8.4 2
 
0.1%
8.5 1
 
0.1%
8.7 2
 
0.1%
8.8 2
 
0.1%
9 30
1.9%
9.05 1
 
0.1%
9.1 23
 
1.4%
9.2 72
4.5%
9.233333333 1
 
0.1%
9.25 1
 
0.1%
ValueCountFrequency (%)
14.9 1
 
0.1%
14 7
0.4%
13.6 4
0.2%
13.56666667 1
 
0.1%
13.5 1
 
0.1%
13.4 3
0.2%
13.3 3
0.2%
13.2 1
 
0.1%
13.1 2
 
0.1%
13 6
0.4%

quality
Real number (ℝ)

Distinct7
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.63625
Minimum3
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.6 KiB
2023-03-24T17:38:18.301251image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q15
median6
Q36
95-th percentile7
Maximum8
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.80711581
Coefficient of variation (CV)0.14320085
Kurtosis0.2997729
Mean5.63625
Median Absolute Deviation (MAD)1
Skewness0.21710675
Sum9023.6362
Variance0.65143594
MonotonicityNot monotonic
2023-03-24T17:38:18.572506image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5 681
42.5%
6 639
39.9%
7 199
 
12.4%
4 53
 
3.3%
8 18
 
1.1%
3 10
 
0.6%
5.63625 1
 
0.1%
ValueCountFrequency (%)
3 10
 
0.6%
4 53
 
3.3%
5 681
42.5%
5.63625 1
 
0.1%
6 639
39.9%
7 199
 
12.4%
8 18
 
1.1%
ValueCountFrequency (%)
8 18
 
1.1%
7 199
 
12.4%
6 639
39.9%
5.63625 1
 
0.1%
5 681
42.5%
4 53
 
3.3%
3 10
 
0.6%

Interactions

2023-03-24T17:38:02.528281image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:07.962857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:12.290271image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:16.364243image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:22.005234image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:26.552245image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:31.109064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:35.380959image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:39.914319image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:44.417857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:48.906840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:53.406693image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:57.939872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:38:02.888362image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:08.291815image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:12.599777image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:16.736540image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:22.387722image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:26.931776image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:31.455467image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:35.740159image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:40.272688image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:44.784304image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:49.271851image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:53.776309image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:58.293291image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:38:03.213215image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:08.610677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:12.893197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:17.056448image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:22.802740image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:27.246907image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:31.773910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:36.071048image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:40.591320image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:45.119638image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:49.755848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:54.105862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:58.611853image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:38:03.568314image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:08.929977image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:13.227295image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:17.461276image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:23.169399image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:27.580797image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:32.098072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:36.420011image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:40.961152image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:45.494415image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:50.107129image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:54.463087image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:58.963818image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:38:03.875145image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:09.236827image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:13.517976image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:17.840451image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:23.521105image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:27.899705image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:32.403052image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:36.747745image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:41.292670image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:45.833677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:50.413791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:54.790082image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:59.282717image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:38:04.215661image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:09.551411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:13.849705image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:18.351962image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:23.894352image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:28.230566image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:32.734352image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:37.075213image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:41.627619image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:46.173956image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:50.752889image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:55.147652image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:59.619084image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:38:04.563287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:09.965268image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:14.165266image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:18.857210image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:24.216614image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:28.553165image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:33.050817image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:37.407951image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:41.972515image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:46.507375image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:51.083853image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:55.486392image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:38:00.117523image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:38:04.897931image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:10.287638image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:14.477221image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:19.474038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:24.543782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:28.897714image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:33.381696image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:37.744283image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:42.319162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:46.865509image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:51.421206image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:55.850059image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:38:00.476436image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:38:05.237444image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:10.628189image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:14.820516image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:19.912957image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:24.880020image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:29.242940image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:33.704254image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:38.073161image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:42.643791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:47.193878image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:51.750561image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:56.180575image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:38:00.819284image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:38:05.579463image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:10.967531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:15.130846image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:20.407781image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:25.221910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:29.586363image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:34.047924image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:38.417668image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:42.993178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:47.543518image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:52.088247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:56.539237image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:38:01.186883image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:38:05.919976image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:11.288628image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:15.433972image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:20.823001image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:25.558473image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:30.134491image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:34.361608image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:38.748817image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:43.326962image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:47.890251image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:52.407611image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:56.882002image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:38:01.513476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:38:06.259895image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:11.608574image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:15.758567image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:21.223520image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:25.935204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:30.445994image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:34.708021image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:39.110861image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:43.701326image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:48.222352image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:52.745122image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:57.238927image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:38:01.861363image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:38:06.595813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:11.965569image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:16.069392image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:21.626114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:26.243685image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:30.796299image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:35.050862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:39.588701image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:44.061729image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:48.573260image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:53.089537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:37:57.590855image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:38:02.192669image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-03-24T17:38:18.882119image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
IDfixed_acidityvolatile_aciditycitric_acidresidual_sugarchloridesfree_sulfur_dioxidetotal_sulfur_dioxidedensitypHsulphatesalcoholquality
ID1.000-0.285-0.005-0.142-0.118-0.1970.084-0.113-0.4090.142-0.0770.2880.083
fixed_acidity-0.2851.000-0.2770.6610.2200.250-0.173-0.0880.622-0.7060.211-0.0670.114
volatile_acidity-0.005-0.2771.000-0.6110.0320.1560.0210.0940.0260.234-0.326-0.226-0.381
citric_acid-0.1420.661-0.6111.0000.1770.114-0.0760.0100.351-0.5490.3310.0970.214
residual_sugar-0.1180.2200.0320.1771.0000.2110.0750.1450.422-0.0900.0390.1170.033
chlorides-0.1970.2500.1560.1140.2111.0000.0010.1290.409-0.2360.021-0.285-0.188
free_sulfur_dioxide0.084-0.1730.021-0.0760.0750.0011.0000.790-0.0410.1150.046-0.081-0.058
total_sulfur_dioxide-0.113-0.0880.0940.0100.1450.1290.7901.0000.129-0.010-0.000-0.257-0.198
density-0.4090.6220.0260.3510.4220.409-0.0410.1291.000-0.3110.160-0.463-0.177
pH0.142-0.7060.234-0.549-0.090-0.2360.115-0.010-0.3111.000-0.0800.180-0.044
sulphates-0.0770.211-0.3260.3310.0390.0210.046-0.0000.160-0.0801.0000.2080.377
alcohol0.288-0.067-0.2260.0970.117-0.285-0.081-0.257-0.4630.1800.2081.0000.479
quality0.0830.114-0.3810.2140.033-0.188-0.058-0.198-0.177-0.0440.3770.4791.000

Missing values

2023-03-24T17:38:07.082362image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-24T17:38:07.761241image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDfixed_acidityvolatile_aciditycitric_acidresidual_sugarchloridesfree_sulfur_dioxidetotal_sulfur_dioxidedensitypHsulphatesalcoholquality
017.40.700.001.90.07611.034.00.99783.5100000.569.45.0
127.80.880.002.60.09825.067.00.99683.2000000.689.85.0
237.80.760.042.30.09215.054.00.99703.3111060.659.85.0
3411.20.280.561.90.07517.060.00.99803.1600000.589.86.0
457.40.700.001.90.07611.034.00.99783.5100000.569.45.0
567.40.660.001.80.07513.040.00.99783.5100000.569.45.0
677.90.600.061.60.06915.059.00.99643.3000000.469.45.0
787.30.650.001.20.06515.021.00.99463.3900000.4710.07.0
897.80.580.022.00.0739.018.00.99683.3600000.579.57.0
9107.50.500.366.10.07117.0102.00.99783.3500000.8010.55.0
IDfixed_acidityvolatile_aciditycitric_acidresidual_sugarchloridesfree_sulfur_dioxidetotal_sulfur_dioxidedensitypHsulphatesalcoholquality
159115926.3000000.5500.151.80.07726.035.00.993143.320.8211.66.0
159215935.4000000.7400.091.70.08916.026.00.994023.670.5611.66.0
159315946.3000000.5100.132.30.07629.040.00.995743.420.7511.06.0
159415956.8000000.6200.081.90.06828.038.00.996513.420.829.56.0
159515968.3198750.6000.082.00.09032.044.00.994903.450.5810.55.0
159615975.9000000.5500.102.20.06239.051.00.995123.520.7611.26.0
159715986.3000000.5100.132.30.07629.040.00.995743.420.7511.06.0
159815995.9000000.6450.122.00.07532.044.00.995473.570.7110.25.0
159916006.0000000.3100.473.60.06718.042.00.995493.390.6611.06.0
160016017.2000000.3900.442.60.06622.048.00.994943.300.8411.56.0